Analytical study on Collaborative Filtering techniques for Location-based Recommendation

The popularity of location-based social networks provides us with a new platform to understand users’ behavior and preferences based on their location histories. Social networking applications have become very important web services that provide Internet-based platforms for their users to interact with their friends. With the advances in the location-aware hardware and software technologies, location-based social networking applications have been pro-posed to provide services for their users, taking into account both the spatial and social aspects. Location as one of the most important components of user context implies extensive knowledge about an individual’s interests and behavior, thereby providing us with opportunities to better understand users in a social structure according to not only online user behavior but also the user mobility and activities in the physical world. Under many such circumstances, a location recommender system is a valuable but unique application in location-based social networking services, in terms of what a recommendation is and where a recommendation is to be made. Collaborative filtering (CF) technique for recommendation becomes one of the popular recommendation techniques for location recommendation. This analysis presents a comparative study on different collaborative filtering methods like Hypertext Induced Topic Search (HITS) based model, CF with Collaborative Location and Activity Recommendations (CLAR) and candidate selection method used for location recommendation.